Segmentation and Validation of Liver Tumors
DOI:
https://doi.org/10.14738/aivp.83.8451Keywords:
Tumor detection, extraction, validation, CT, FMM, GLCM, Accuracy, False Positive and Overlap ErrorAbstract
Automatic detection, extraction and validation of tumors from the segmented Computed Tomography (CT) liver is a crucial task. Segmentation of tumors provides a landmark to detect and extract tumors from segmented liver image. In this proposed work, Otsu thresholding technique, Level set method, Super pixel-Overlay method and Fast Marching Method (FMM) are used to extract the tumors. Later, Texture features are extracted from the segmented tumors using Gray Level Co-Occurance Matrix (GLCM) and these tumors are validated using Euclidean distance. The work is evaluated on 3DircadB and Clumax dataset using Accuracy, False Positive and Overlap Error parameters. Self relative study and empirical comparative study are performed and results are tabulated. The observation is that Fast Marching method has performed better than existing methods.
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